.png){kind=icon}
返回
An Interruption Overvoltage Estimation Method With Improved Swarm Intelligence Algorithm for Modular DC Circuit Breakers
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2023.3342227.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Modular dc circuit breakers (MDCCBs) are the key equipment for dc networks, and their interruption overvoltage is an important aspect of protecting and monitoring MDCCBs. This article analyzes the existing problems of the MDCCB interruption overvoltage estimation from two aspects, including a metal-oxide varistor (MOV) numerical model and a parameter acquisition method. On this basis, an improved interruption overvoltage estimation method is proposed. This method includes an improved MOV numerical model and an improved swarm intelligence algorithm (ISIA), and the proposed ISIA has the advantage of the Levenberg-Marquardt algorithm (LM) and the swarm intelligence algorithm (SIA). The study results, based on the actual MDCCB interruption test whose rated voltage is 10 kV, show that the proposed ISIA can effectively obtain the optimal parameters of the improved MOV numerical model, and this numerical model reflects the relationship between the interruption overvoltage and current of MDCCB well. Compared with traditional MOV numerical models and parameter acquisition methods, the proposed method achieves a maximum absolute error between the estimated and actual interruption overvoltage of only 0.30 kV during the MDCCB interruption period.
Keyword:
DC networks
interruption overvoltage estimation
metal-oxide varistor (MOV)
modular dc circuit breaker (MDCCB)
swarm intelligence algorithm (SIA)
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
机构
引用论文
.png){kind=icon}
Study on the Dielectric Recovery Strength of Vacuum Interrupter in MVDC Circuit BreakerMVDC断路器真空灭弧室介质恢复强度的研究
PREAI
分享收藏
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Mixture of Ensemble Empirical Mode Decomposition and GWO-SVR Model基于集合经验模态分解和gwo-svr模型的锂离子电池剩余寿命预测
PREAI
分享收藏
查看更多内容